






"""simple docstring"""





import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
  from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


_SCREAMING_SNAKE_CASE   :							int          =		logging.get_logger(__name__)  # pylint: disable=invalid-name

_SCREAMING_SNAKE_CASE   :							List[str]          =		256






class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					["""melgan"""]






    def __init__(			self	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				SpectrogramNotesEncoder			,		__SCREAMING_SNAKE_CASE	:				SpectrogramContEncoder			,		__SCREAMING_SNAKE_CASE	:				TaFilmDecoder			,		__SCREAMING_SNAKE_CASE	:				DDPMScheduler			,		__SCREAMING_SNAKE_CASE	:				OnnxRuntimeModel if is_onnx_available() else Any			,		)	->						None:
      super().__init__()

      # From MELGAN
      lowerCamelCase_       	=		math.log(1e-5   )  # Matches MelGAN training.
      lowerCamelCase_       	=		4.0  # Largest value for most examples
      lowerCamelCase_       	=		128

      self.register_modules(
          notes_encoder=__SCREAMING_SNAKE_CASE			,		continuous_encoder=__SCREAMING_SNAKE_CASE			,		decoder=__SCREAMING_SNAKE_CASE			,		scheduler=__SCREAMING_SNAKE_CASE			,		melgan=__SCREAMING_SNAKE_CASE			,		)






    def   UpperCamelCase			(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				Dict=(-1.0, 1.0)			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=False   )	->						Optional[int]:
      lowerCamelCase_	,      lowerCamelCase_       	=		output_range
      if clip:
        lowerCamelCase_       	=		torch.clip(__SCREAMING_SNAKE_CASE			,		self.min_value			,		self.max_value   )
      # Scale to [0, 1].
      lowerCamelCase_       	=		(features - self.min_value) / (self.max_value - self.min_value)
      # Scale to [min_out, max_out].
      return zero_one * (max_out - min_out) + min_out






    def   UpperCamelCase			(			self	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				Tuple=(-1.0, 1.0)			,		__SCREAMING_SNAKE_CASE	:				List[Any]=False   )	->						Dict:
      lowerCamelCase_	,      lowerCamelCase_       	=		input_range
      lowerCamelCase_       	=		torch.clip(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ) if clip else outputs
      # Scale to [0, 1].
      lowerCamelCase_       	=		(outputs - min_out) / (max_out - min_out)
      # Scale to [self.min_value, self.max_value].
      return zero_one * (self.max_value - self.min_value) + self.min_value






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				str   )	->						Optional[Any]:
      lowerCamelCase_       	=		input_tokens > 0
      lowerCamelCase_	,      lowerCamelCase_       	=		self.notes_encoder(
          encoder_input_tokens=__SCREAMING_SNAKE_CASE			,		encoder_inputs_mask=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_	,      lowerCamelCase_       	=		self.continuous_encoder(
          encoder_inputs=__SCREAMING_SNAKE_CASE			,		encoder_inputs_mask=__SCREAMING_SNAKE_CASE   )

      return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]   )	->						Optional[int]:
      lowerCamelCase_       	=		noise_time
      if not torch.is_tensor(__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		torch.tensor([timesteps]			,		dtype=torch.long			,		device=input_tokens.device   )
      elif torch.is_tensor(__SCREAMING_SNAKE_CASE   ) and len(timesteps.shape   ) == 0:
        lowerCamelCase_       	=		timesteps[None].to(input_tokens.device   )

      # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
      lowerCamelCase_       	=		timesteps * torch.ones(input_tokens.shape[0]			,		dtype=timesteps.dtype			,		device=timesteps.device   )

      lowerCamelCase_       	=		self.decoder(
          encodings_and_masks=__SCREAMING_SNAKE_CASE			,		decoder_input_tokens=__SCREAMING_SNAKE_CASE			,		decoder_noise_time=__SCREAMING_SNAKE_CASE   )
      return logits






    @torch.no_grad()
    def __call__(			self	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				List[List[int]]			,		__SCREAMING_SNAKE_CASE	:				Optional[torch.Generator] = None			,		__SCREAMING_SNAKE_CASE	:				int = 100			,		__SCREAMING_SNAKE_CASE	:				bool = True			,		__SCREAMING_SNAKE_CASE	:				str = "numpy"			,		__SCREAMING_SNAKE_CASE	:				Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,		__SCREAMING_SNAKE_CASE	:				int = 1			,		)	->						Union[AudioPipelineOutput, Tuple]:
      if (callback_steps is None) or (
          callback_steps is not None and (not isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ) or callback_steps <= 0)
      ):
        raise ValueError(
            F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
            F''' {type(__SCREAMING_SNAKE_CASE   )}.'''   )

      lowerCamelCase_       	=		np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]			,		dtype=np.floataa   )
      lowerCamelCase_       	=		np.zeros([1, 0, self.n_dims]			,		np.floataa   )
      lowerCamelCase_       	=		torch.ones((1, TARGET_FEATURE_LENGTH)			,		dtype=__SCREAMING_SNAKE_CASE			,		device=self.device   )

      for i, encoder_input_tokens in enumerate(__SCREAMING_SNAKE_CASE   ):
        if i == 0:
          lowerCamelCase_       	=		torch.from_numpy(pred_mel[:1].copy()   ).to(
              device=self.device			,		dtype=self.decoder.dtype   )
          # The first chunk has no previous context.
          lowerCamelCase_       	=		torch.zeros((1, TARGET_FEATURE_LENGTH)			,		dtype=__SCREAMING_SNAKE_CASE			,		device=self.device   )
        else:
          # The full song pipeline does not feed in a context feature, so the mask
          # will be all 0s after the feature converter. Because we know we're
          # feeding in a full context chunk from the previous prediction, set it
          # to all 1s.
          lowerCamelCase_       	=		ones

        lowerCamelCase_       	=		self.scale_features(
            __SCREAMING_SNAKE_CASE			,		output_range=[-1.0, 1.0]			,		clip=__SCREAMING_SNAKE_CASE   )

        lowerCamelCase_       	=		self.encode(
            input_tokens=torch.IntTensor([encoder_input_tokens]   ).to(device=self.device   )			,		continuous_inputs=__SCREAMING_SNAKE_CASE			,		continuous_mask=__SCREAMING_SNAKE_CASE			,		)

        # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
        lowerCamelCase_       	=		randn_tensor(
            shape=encoder_continuous_inputs.shape			,		generator=__SCREAMING_SNAKE_CASE			,		device=self.device			,		dtype=self.decoder.dtype			,		)

        # set step values
        self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE   )

        # Denoising diffusion loop
        for j, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
          lowerCamelCase_       	=		self.decode(
              encodings_and_masks=__SCREAMING_SNAKE_CASE			,		input_tokens=__SCREAMING_SNAKE_CASE			,		noise_time=t / self.scheduler.config.num_train_timesteps			,		)

          # Compute previous output: x_t -> x_t-1
          lowerCamelCase_       	=		self.scheduler.step(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE   ).prev_sample

        lowerCamelCase_       	=		self.scale_to_features(__SCREAMING_SNAKE_CASE			,		input_range=[-1.0, 1.0]   )
        lowerCamelCase_       	=		mel[:1]
        lowerCamelCase_       	=		mel.cpu().float().numpy()

        lowerCamelCase_       	=		np.concatenate([full_pred_mel, pred_mel[:1]]			,		axis=1   )

        # call the callback, if provided
        if callback is not None and i % callback_steps == 0:
          callback(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

        logger.info('Generated segment'			,		__SCREAMING_SNAKE_CASE   )

      if output_type == "numpy" and not is_onnx_available():
        raise ValueError(
            'Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.'   )
      elif output_type == "numpy" and self.melgan is None:
        raise ValueError(
            'Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.'   )

      if output_type == "numpy":
        lowerCamelCase_       	=		self.melgan(input_features=full_pred_mel.astype(np.floataa   )   )
      else:
        lowerCamelCase_       	=		full_pred_mel

      if not return_dict:
        return (output,)

      return AudioPipelineOutput(audios=__SCREAMING_SNAKE_CASE   )












"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					KandinskyImgaImgPipeline
    SCREAMING_SNAKE_CASE		:	Tuple		=					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
    SCREAMING_SNAKE_CASE		:	int		=					[
        """prompt""",
        """negative_prompt""",
        """image_embeds""",
        """negative_image_embeds""",
        """image""",
    ]
    SCREAMING_SNAKE_CASE		:	List[str]		=					[
        """generator""",
        """height""",
        """width""",
        """strength""",
        """guidance_scale""",
        """negative_prompt""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    SCREAMING_SNAKE_CASE		:	Any		=					False






    @property
    def   UpperCamelCase			(			self	:				List[str]   )	->						str:
      return 32






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      return 32






    @property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						str:
      return self.time_input_dim






    @property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[Any]:
      return self.time_input_dim * 4






    @property
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      return 100






    @property
    def   UpperCamelCase			(			self	:				Any   )	->						List[Any]:
      lowerCamelCase_       	=		XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
      return tokenizer






    @property
    def   UpperCamelCase			(			self	:				str   )	->						Optional[Any]:
      torch.manual_seed(0   )
      lowerCamelCase_       	=		MCLIPConfig(
          numDims=self.cross_attention_dim			,		transformerDimensions=self.text_embedder_hidden_size			,		hidden_size=self.text_embedder_hidden_size			,		intermediate_size=37			,		num_attention_heads=4			,		num_hidden_layers=5			,		vocab_size=1005			,		)

      lowerCamelCase_       	=		MultilingualCLIP(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		text_encoder.eval()

      return text_encoder






    @property
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      torch.manual_seed(0   )

      lowerCamelCase_       	=		{
          'in_channels': 4,
          # Out channels is double in channels because predicts mean and variance
          'out_channels': 8,
          'addition_embed_type': 'text_image',
          'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
          'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
          'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
          'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
          'layers_per_block': 1,
          'encoder_hid_dim': self.text_embedder_hidden_size,
          'encoder_hid_dim_type': 'text_image_proj',
          'cross_attention_dim': self.cross_attention_dim,
          'attention_head_dim': 4,
          'resnet_time_scale_shift': 'scale_shift',
          'class_embed_type': None,
      }

      lowerCamelCase_       	=		UNetaDConditionModel(**__SCREAMING_SNAKE_CASE   )
      return model






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }






    @property
    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[Any]:
      torch.manual_seed(0   )
      lowerCamelCase_       	=		VQModel(**self.dummy_movq_kwargs   )
      return model






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						List[Any]:
      lowerCamelCase_       	=		self.dummy_text_encoder
      lowerCamelCase_       	=		self.dummy_tokenizer
      lowerCamelCase_       	=		self.dummy_unet
      lowerCamelCase_       	=		self.dummy_movq

      lowerCamelCase_       	=		{
          'num_train_timesteps': 1000,
          'beta_schedule': 'linear',
          'beta_start': 0.00_085,
          'beta_end': 0.012,
          'clip_sample': False,
          'set_alpha_to_one': False,
          'steps_offset': 0,
          'prediction_type': 'epsilon',
          'thresholding': False,
      }

      lowerCamelCase_       	=		DDIMScheduler(**__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		{
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Dict=0   )	->						str:
      lowerCamelCase_       	=		floats_tensor((1, self.cross_attention_dim)			,		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		floats_tensor((1, self.cross_attention_dim)			,		rng=random.Random(seed + 1   )   ).to(__SCREAMING_SNAKE_CASE   )
      # create init_image
      lowerCamelCase_       	=		floats_tensor((1, 3, 64, 64)			,		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		image.cpu().permute(0			,		2			,		3			,		1   )[0]
      lowerCamelCase_       	=		Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert('RGB'   ).resize((256, 256)   )

      if str(__SCREAMING_SNAKE_CASE   ).startswith('mps'   ):
        lowerCamelCase_       	=		torch.manual_seed(__SCREAMING_SNAKE_CASE   )
      else:
        lowerCamelCase_       	=		torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		{
          'prompt': 'horse',
          'image': init_image,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 64,
          'width': 64,
          'num_inference_steps': 10,
          'guidance_scale': 7.0,
          'strength': 0.2,
          'output_type': 'np',
      }
      return inputs






    def   UpperCamelCase			(			self	:				Tuple   )	->						Any:
      lowerCamelCase_       	=		'cpu'

      lowerCamelCase_       	=		self.get_dummy_components()

      lowerCamelCase_       	=		self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		pipe.to(__SCREAMING_SNAKE_CASE   )

      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )   )
      lowerCamelCase_       	=		output.images

      lowerCamelCase_       	=		pipe(
          **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )			,		return_dict=__SCREAMING_SNAKE_CASE			,		)[0]

      lowerCamelCase_       	=		image[0, -3:, -3:, -1]
      lowerCamelCase_       	=		image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      lowerCamelCase_       	=		np.array(
          [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]   )
      assert (
          np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
      ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
      ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class 	a		(   unittest.TestCase						):






    def   UpperCamelCase			(			self	:				Tuple   )	->						List[str]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def   UpperCamelCase			(			self	:				Any   )	->						int:
      lowerCamelCase_       	=		load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinsky/kandinsky_img2img_frog.npy'   )

      lowerCamelCase_       	=		load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
      lowerCamelCase_       	=		'A red cartoon frog, 4k'

      lowerCamelCase_       	=		KandinskyPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-prior'			,		torch_dtype=torch.floataa   )
      pipe_prior.to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		KandinskyImgaImgPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1'			,		torch_dtype=torch.floataa   )
      lowerCamelCase_       	=		pipeline.to(__SCREAMING_SNAKE_CASE   )

      pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.Generator(device='cpu'   ).manual_seed(0   )
      lowerCamelCase_	,      lowerCamelCase_       	=		pipe_prior(
          __SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		num_inference_steps=5			,		negative_prompt=''			,		).to_tuple()

      lowerCamelCase_       	=		pipeline(
          __SCREAMING_SNAKE_CASE			,		image=__SCREAMING_SNAKE_CASE			,		image_embeds=__SCREAMING_SNAKE_CASE			,		negative_image_embeds=__SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		num_inference_steps=100			,		height=768			,		width=768			,		strength=0.2			,		output_type='np'			,		)

      lowerCamelCase_       	=		output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )




